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<title>LocalMCP - Knowledge Base Integration Plan</title>
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<h1>LocalMCP × Knowledge Base</h1>
<p class="subtitle">Integrating LocalMCP with Your Existing AI Knowledge Infrastructure</p>
<div class="mermaid">
graph TB
subgraph "Existing Knowledge Base"
KB["/02_knowledge/"]
SS["Specialist System<br/>11 Core Concepts<br/>4-Layer Architecture"]
LIB["Librarian<br/>17 Document Pairs<br/>88 Searchable Chunks"]
KB --> SS
KB --> LIB
end
subgraph "LocalMCP Integration"
MCP["LocalMCP Core"]
KS["Knowledge MCP Server"]
SO["Semantic Orchestrator"]
CACHE["Intelligent Cache"]
end
subgraph "Enhanced Capabilities"
NLP["Natural Language Interface"]
CTX["Context-Aware Responses"]
LEARN["Continuous Learning"]
MULTI["Multi-Modal Understanding"]
end
subgraph "MCP Tools"
T1["mcp_knowledge_search"]
T2["mcp_concept_explain"]
T3["mcp_implementation_guide"]
T4["mcp_learning_path"]
end
SS -.-> KS
LIB -.-> KS
KS --> MCP
MCP --> SO
MCP --> CACHE
SO --> T1
SO --> T2
SO --> T3
SO --> T4
MCP --> NLP
MCP --> CTX
MCP --> LEARN
MCP --> MULTI
style KB fill:#E9D8FD,stroke:#B794F4,stroke-width:3px
style SS fill:#FED7E2,stroke:#F687B3,stroke-width:2px
style LIB fill:#BEE3F8,stroke:#63B3ED,stroke-width:2px
style MCP fill:#C6F6D5,stroke:#68D391,stroke-width:3px
style KS fill:#FEFCBF,stroke:#F6E05E,stroke-width:2px
style SO fill:#FED7AA,stroke:#F6AD55,stroke-width:2px
style CACHE fill:#E0E7FF,stroke:#A5B4FC,stroke-width:2px
</div>
<div class="section">
<h2>
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<path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-2 15l-5-5 1.41-1.41L10 14.17l7.59-7.59L19 8l-9 9z"/>
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Integration Overview
</h2>
<p>Your existing knowledge base at <code>/Users/lech/02_knowledge/</code> provides a perfect foundation for LocalMCP's semantic capabilities. By creating a custom MCP server that interfaces with both your Specialist System and Librarian, LocalMCP can leverage this rich knowledge while adding advanced features.</p>
<div class="stats-grid">
<div class="stat-card">
<span class="stat-value">11</span>
<span class="stat-label">Core Concepts</span>
</div>
<div class="stat-card">
<span class="stat-value">88</span>
<span class="stat-label">Searchable Chunks</span>
</div>
<div class="stat-card">
<span class="stat-value">17</span>
<span class="stat-label">Document Pairs</span>
</div>
<div class="stat-card">
<span class="stat-value">∞</span>
<span class="stat-label">Possibilities</span>
</div>
</div>
</div>
<div class="section">
<h2>
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Integration Architecture
</h2>
<h3>1. Knowledge MCP Server</h3>
<p>A custom MCP server that wraps your existing knowledge base interfaces:</p>
<div class="code-block">
# /Users/lech/LocalMCP/mcp_servers/knowledge_server.py
import sys
sys.path.append('/Users/lech/02_knowledge')
from specialist_system.retrieval_interface import SpecialistKnowledgeRetriever
from librarian.librarian_retrieval import LibrarianRetrieval, LibrarianAssistant
from mcp.server import MCPServer, Tool
class KnowledgeMCPServer(MCPServer):
def __init__(self):
super().__init__("knowledge-base")
self.specialist = SpecialistKnowledgeRetriever()
self.librarian = LibrarianRetrieval()
self.assistant = LibrarianAssistant()
def get_tools(self):
return [
Tool(
name="search_knowledge",
description="Search across all knowledge sources",
parameters={
"query": {"type": "string", "required": True},
"source": {"type": "string", "enum": ["all", "specialist", "librarian"]},
"max_results": {"type": "integer", "default": 5}
}
),
Tool(
name="get_concept",
description="Get detailed information about a specialist concept",
parameters={
"concept": {"type": "string", "required": True}
}
),
Tool(
name="build_implementation_guide",
description="Generate implementation guide based on knowledge",
parameters={
"topic": {"type": "string", "required": True},
"level": {"type": "string", "enum": ["beginner", "intermediate", "advanced"]}
}
)
]</div>
<h3>2. Enhanced Semantic Orchestration</h3>
<p>LocalMCP's semantic orchestrator gains special awareness of your knowledge domains:</p>
<div class="code-block">
# Enhanced orchestrator with knowledge-aware routing
class KnowledgeAwareOrchestrator(SemanticOrchestrator):
def __init__(self, knowledge_server):
super().__init__()
self.knowledge_server = knowledge_server
async def route_query(self, query: str) -> ToolSelection:
# Check if query relates to specialist concepts
concepts = await self.extract_concepts(query)
if concepts:
# Prioritize specialist system tools
return self.select_specialist_tools(concepts)
# Check for document-based queries
if self.is_documentation_query(query):
# Use librarian tools
return self.select_librarian_tools(query)
# Default semantic routing
return await super().route_query(query)</div>
</div>
<div class="section">
<h2>
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Key Integration Features
</h2>
<div class="integration-grid">
<div class="integration-card">
<h4>Unified Knowledge Access</h4>
<p>Single interface to query both specialist concepts and document library through natural language.</p>
<div class="code-block">
# Example usage
result = await mcp.execute(
"How do I implement hybrid retrieval
for a Natural AI Specialist?"
)
# Automatically uses both knowledge sources</div>
</div>
<div class="integration-card">
<h4>Context-Aware Responses</h4>
<p>LocalMCP builds rich context by combining specialist insights with relevant documentation.</p>
<div class="code-block">
# Combines:
- Specialist concept definitions
- Implementation examples
- Related documents
- Hardware optimizations
→ Comprehensive answer</div>
</div>
<div class="integration-card">
<h4>Learning Path Integration</h4>
<p>Automatically generates learning sequences based on your existing knowledge structure.</p>
<div class="code-block">
# Leverages your learning paths
path = await mcp.execute(
"Create a learning path for
building MCP servers"
)
# Uses librarian sequence + concepts</div>
</div>
<div class="integration-card">
<h4>Semantic Caching</h4>
<p>Cache responses based on concept similarity, dramatically improving performance.</p>
<div class="code-block">
# If asked about "RRF algorithm"
# Can return cached "Reciprocal Rank Fusion"
# Based on semantic similarity > 0.95</div>
</div>
<div class="integration-card">
<h4>Multi-Modal Enhancement</h4>
<p>Combine your text knowledge with diagrams, code examples, and visual explanations.</p>
<div class="code-block">
# Generates Mermaid diagrams
# from architecture descriptions
diagram = await mcp.visualize(
concept="4-Layer Architecture"
)</div>
</div>
<div class="integration-card">
<h4>Continuous Learning</h4>
<p>LocalMCP can update your knowledge base with new insights discovered during interactions.</p>
<div class="code-block">
# Discovers new implementation pattern
await knowledge_server.add_insight({
"type": "implementation_pattern",
"concept": "MCP Tool Chaining",
"example": discovered_pattern
})</div>
</div>
</div>
</div>
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<h2>
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Implementation Timeline
</h2>
<h3>Phase 1: Knowledge MCP Server (Days 1-3)</h3>
<ul class="feature-list">
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Wrap existing interfaces</strong> - Create MCP server that exposes your retrieval interfaces as MCP tools
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Implement search tools</strong> - Unified search, concept retrieval, and context building
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Add caching layer</strong> - Integrate with LocalMCP's multi-layer cache
</div>
</li>
</ul>
<h3>Phase 2: Enhanced Orchestration (Days 4-5)</h3>
<ul class="feature-list">
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Knowledge-aware routing</strong> - Teach orchestrator about your knowledge domains
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Concept extraction</strong> - Identify specialist concepts in queries
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Semantic similarity</strong> - Use your concept relationships for better tool selection
</div>
</li>
</ul>
<h3>Phase 3: Advanced Features (Days 6-7)</h3>
<ul class="feature-list">
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Learning path generation</strong> - Dynamic paths based on user queries
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Visual explanations</strong> - Auto-generate diagrams from concepts
</div>
</li>
<li>
<span class="feature-icon">✓</span>
<div>
<strong>Knowledge updates</strong> - Feed discoveries back into your KB
</div>
</li>
</ul>
</div>
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<h2>
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<path d="M12 2l3.09 6.26L22 9.27l-5 4.87 1.18 6.88L12 17.77l-6.18 3.25L7 14.14 2 9.27l6.91-1.01L12 2z"/>
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Expected Benefits
</h2>
<div class="benefit-box">
<h4>🚀 Instant Knowledge Access</h4>
<p>Your 88 document chunks and 11 core concepts become instantly accessible through natural language, with sub-second response times thanks to semantic caching.</p>
</div>
<div class="benefit-box">
<h4>🧠 Intelligent Context Building</h4>
<p>LocalMCP automatically combines specialist concepts with relevant documentation, creating comprehensive contexts that rival human experts.</p>
</div>
<div class="benefit-box">
<h4>📈 Continuous Improvement</h4>
<p>Every interaction potentially enriches your knowledge base with new patterns, examples, and insights discovered by the AI agent.</p>
</div>
<div class="benefit-box">
<h4>🔗 Seamless Integration</h4>
<p>Your existing Python interfaces work as-is. LocalMCP wraps them in MCP protocol without requiring any changes to your current code.</p>
</div>
</div>
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